Abstract
Feature selection refers to the problem of selecting the input attributes or features that are most effective to predict the sample categories. In this regard, a feature selection method is presented based on fuzzy-rough sets by maximizing both relevance and significance of the selected features. The paper also presents different feature evaluation criteria such as dependency, relevance, redundancy and significance for attribute selection task using fuzzy-rough sets. The performance of different rough set models is compared with that of some existing feature evaluation indices based on the predictive accuracy of nearest neighbor rule, support vector machine and decision tree. The effectiveness of fuzzy-rough set based attribute selection method, along with a comparison with existing feature evaluation indices and different rough set models, is demonstrated on two benchmark and two microarray gene expression data sets.
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Maji, P., Garai, P. (2012). Fuzzy-Rough MRMS Method for Relevant and Significant Attribute Selection. In: Greco, S., Bouchon-Meunier, B., Coletti, G., Fedrizzi, M., Matarazzo, B., Yager, R.R. (eds) Advances on Computational Intelligence. IPMU 2012. Communications in Computer and Information Science, vol 297. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31709-5_32
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DOI: https://doi.org/10.1007/978-3-642-31709-5_32
Publisher Name: Springer, Berlin, Heidelberg
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